论文概要
研究领域: CV 作者: Fabio Tosi, Luca Bartolomei, Matteo Poggi, Stefano Mattoccia 发布时间: 2026-07-09 arXiv: 2607.08771
中文摘要
单目深度估计通过基础模型实现了强大的零样本泛化能力，取得了显著进展，但其计算需求使其难以部署在嵌入式和移动平台上。轻量级替代方案存在，但几乎完全基于单域、自监督范式开发，在域迁移时静默失效。我们提出ZipDepth，一种紧凑的单目深度网络，通过将高效的可重参数化编码器-解码器与大规模多域训练集上的基础模型知识蒸馏相结合，弥合了这一差距。ZipDepth仅含610万参数，可在服务器GPU到功耗受限设备上实时运行，在五个基准测试中实现了轻量级模型中零样本精度与部署效率之间的最佳权衡，朝着参数规模50倍于己的基础模型精度迈出了重要一步。
原文摘要
Monocular depth estimation has seen remarkable progress through foundation models achieving robust zero-shot generalization, yet their computational demands place them far beyond the reach of embedded and mobile platforms. Lightweight alternatives exist, but have been developed almost exclusively within single-domain, self-supervised paradigms, failing silently under domain shift. We present ZipDepth, a compact monocular depth network that bridges this gap by combining an efficient reparameterizable encoder-decoder with large-scale knowledge distillation from a foundation model over a large multi-domain training set. Comprising just 6.1M parameters, ZipDepth runs at real-time rates from server GPUs to power-constrained devices, achieving the best trade-off between zero-shot accuracy and de…
— 自动采集于 2026-07-12
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